Attach required packages:

Part 1: Principal components analysis (PCA)

Principal components analysis is an ordination method allowing us to glean as much about our multivariate data as possible in a simplified number of dimensions.

Here, we’ll use the penguins data within the palmerpenguins R package to explore variable relationships and clustering by species in a PCA biplot. For this example with PCA, we will only use the structure size measurements (bill length and depth, mass, and flipper length).

penguin_pca <- penguins %>% 
  dplyr::select(body_mass_g, ends_with("_mm")) %>%
  tidyr::drop_na() %>%
  scale() %>%
  prcomp()

penguin_complete <- penguins %>% 
  drop_na(body_mass_g, ends_with("_mm"))

autoplot(penguin_pca,
         data = penguin_complete,
         loadings = TRUE,
         colour = 'species',
         loadings.label = TRUE,
         loadings.colour = "black",
         loadings.label.colour = "black",
         loadings.label.vjust = -0.5
         ) +
  scale_color_manual(values = c("blue","purple","orange")) +
  scale_fill_manual(values = c("blue","purple","orange")) +
  theme_minimal()

# It's not perfect, but it's enough for now...

Part 2: ggplot customization & reading in different file types

We spent some time in ESM 206 customizing our data visualizations. Let’s add some more tools, including: - Highlight spaghetti plots with gghighlight - An interactive graph with plotly - A universe of color palettes in paletteer

Here, we’ll also read in stored .txt and .xlsx, and files from a URL to build our toolkit for how to read in data.

Data: NOAA Foreign Fisheries Trade Data

Read in a .xlsx file, & do some wrangling

fish_noaa <- read_excel(here("data","foss_landings.xlsx")) %>% 
  clean_names() %>% 
  mutate(across(where(is.character), tolower)) %>% # convert all characters to lowercase 
  mutate(nmfs_name = str_sub(nmfs_name, end = -4)) %>%  # remove last 3 characters
  filter(confidentiality == "public")

Now, let’s make and customize a graph:

fish_plot <- ggplot(data = fish_noaa, aes(x = year, y = pounds, group = nmfs_name)) +
  geom_line(aes(color = nmfs_name)) +
  theme_minimal()

# Make it interactive:
ggplotly(fish_plot)
# Highlight series based on condition(s):
ggplot(data = fish_noaa, aes(x = year, y = pounds, group = nmfs_name)) +
  geom_line() +
  gghighlight(nmfs_name == "tunas") + # Highlight just tunas
  theme_minimal()

ggplot(data = fish_noaa, aes(x = year, y = pounds, group = nmfs_name)) +
  geom_line(aes(color = nmfs_name)) +
  gghighlight(max(pounds) > 1e8) + # Highlight just tunas
  theme_minimal()

Read in data from a URL, lubridate() refresher, and introducing paletteer for color palettes

  • See paletteer color palettes:
  • Discrete with View(palettes_d_names)
  • Continuous with View(palettes_c_names)

Data: Monroe Water Treatment Plant Daily Electricity Use

Accessed from data.gov

Summary: “Daily energy use (kWh), demand (kW), and volume water treated (million gallons). 2010 through current. A second electric meter and account were added at the plant in March 2013. The usage and demand data from this meter are labeled as”Energy Use 2" and “Peak 2.”

The URL to the CSV file is provided at the website above (or copy from below):

monroe_wt <- read_csv("https://data.bloomington.in.gov/dataset/2c81cfe3-62c2-46ed-8fcf-83c1880301d1/resource/13c8f7aa-af51-4008-80a9-56415c7c931e/download/mwtpdailyelectricitybclear.csv") %>% 
  clean_names()

monroe_ts <- monroe_wt %>% 
  mutate(date = mdy(date)) %>% # Convert date to a stored date class
  mutate(record_month = month(date)) %>% # Add column w/ month number
  mutate(month_name = month.abb[record_month]) %>% # Add column w/ month abbreviation
  mutate(month_name = fct_reorder(month_name, record_month)) # Make month name a factor & reorder based on values in record_month column

ggplot(data = monroe_ts, aes(x = month_name, y = total_k_wh)) +
  geom_jitter(aes(color = month_name), 
              show.legend = FALSE,
              alpha = 0.5, 
              size = 0.3,
              width = 0.2) +
  scale_color_paletteer_d("palettetown::delibird")

Part 3: patchwork for compound figures

Let’s make two quick graphs, & store them. Then combine using patchwork. See more information about the patchwork package HERE.

graph_a <- ggplot(data = penguins, aes(x = body_mass_g, y = flipper_length_mm)) +
  geom_point(aes(size = bill_length_mm, color = bill_depth_mm), show.legend = FALSE) +
  scale_color_paletteer_c("grDevices::RdBu")

graph_b <- ggplot(data = penguins, aes(x = species, y = flipper_length_mm)) +
  geom_jitter(aes(color = flipper_length_mm), show.legend = FALSE)

# Use | to put graphs side by side, and / to put one over the other. 
graph_a | graph_b

# Store the output, apply updates across all graphs with &
graph_c <- graph_a / graph_b & theme_minimal()

# Export as a .png with ggsave:
ggsave(here("fig","graph_c.png"), width = 5, height = 5)

# Get even wilder (not a real example):
graph_c | graph_a

# Or something like: 

(graph_a | graph_b | graph_a) / (graph_b | graph_a) & theme_dark()

Part 4: Shiny app basics (see app_demo folder)